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Automatic Liver Segmentation on Volumetric CT Images Using Supervoxel-Based Graph Cuts.

Wu W, Zhou Z, Wu S, Zhang Y - Comput Math Methods Med (2016)

Bottom Line: Despite many years of research, automatic liver segmentation remains a challenging task.Thirty abdominal CT images were used to evaluate the accuracy and efficiency of the proposed algorithm.Experimental results show that the proposed method can detect the liver accurately with significant reduction of processing time, especially when dealing with diseased liver cases.

View Article: PubMed Central - PubMed

Affiliation: College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124, China.

ABSTRACT
Accurate segmentation of liver from abdominal CT scans is critical for computer-assisted diagnosis and therapy. Despite many years of research, automatic liver segmentation remains a challenging task. In this paper, a novel method was proposed for automatic delineation of liver on CT volume images using supervoxel-based graph cuts. To extract the liver volume of interest (VOI), the region of abdomen was firstly determined based on maximum intensity projection (MIP) and thresholding methods. Then, the patient-specific liver VOI was extracted from the region of abdomen by using a histogram-based adaptive thresholding method and morphological operations. The supervoxels of the liver VOI were generated using the simple linear iterative clustering (SLIC) method. The foreground/background seeds for graph cuts were generated on the largest liver slice, and the graph cuts algorithm was applied to the VOI supervoxels. Thirty abdominal CT images were used to evaluate the accuracy and efficiency of the proposed algorithm. Experimental results show that the proposed method can detect the liver accurately with significant reduction of processing time, especially when dealing with diseased liver cases.

No MeSH data available.


Related in: MedlinePlus

Flow chart of the proposed 3D liver segmentation method.
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fig1: Flow chart of the proposed 3D liver segmentation method.

Mentions: The flow chart of the proposed approach is illustrated in Figure 1. The proposed segmentation framework consisted of five steps. (1) In the preprocessing step, smoothing and resampling were conducted by using recursive Gaussian filtering and linear interpolation, respectively. (2) The patient-specific liver VOI extraction procedure was performed firstly by determining the region of abdomen using maximum intensity projection (MIP) [28] and thresholding methods, followed by applying a histogram-based adaptive thresholding method and morphological operations. The largest liver slice, which was the axial slice containing the maximal amount of liver tissue, was automatically selected. (3) The generation of supervoxels was performed on the liver VOI using the simple linear iterative clustering (SLIC) method [29]. (4) In the segmentation step, foreground/background seed points for graph cuts were selected on the largest liver slice, and additional background seed points on regions of heart and kidney were extracted automatically on selected heart and kidney slices. Gaussian mixture models (GMMs) were used to estimate the intensity distributions of foreground/background seeds [30]. Subsequently, the graph cuts algorithm [31, 32] was applied to the VOI supervoxels. (5) Finally, morphological opening, cavity filling, and median filtering were used to refine the segmented liver. After being resampled back to the original spatial resolution and size, the segmentation result was output for evaluation.


Automatic Liver Segmentation on Volumetric CT Images Using Supervoxel-Based Graph Cuts.

Wu W, Zhou Z, Wu S, Zhang Y - Comput Math Methods Med (2016)

Flow chart of the proposed 3D liver segmentation method.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4835633&req=5

fig1: Flow chart of the proposed 3D liver segmentation method.
Mentions: The flow chart of the proposed approach is illustrated in Figure 1. The proposed segmentation framework consisted of five steps. (1) In the preprocessing step, smoothing and resampling were conducted by using recursive Gaussian filtering and linear interpolation, respectively. (2) The patient-specific liver VOI extraction procedure was performed firstly by determining the region of abdomen using maximum intensity projection (MIP) [28] and thresholding methods, followed by applying a histogram-based adaptive thresholding method and morphological operations. The largest liver slice, which was the axial slice containing the maximal amount of liver tissue, was automatically selected. (3) The generation of supervoxels was performed on the liver VOI using the simple linear iterative clustering (SLIC) method [29]. (4) In the segmentation step, foreground/background seed points for graph cuts were selected on the largest liver slice, and additional background seed points on regions of heart and kidney were extracted automatically on selected heart and kidney slices. Gaussian mixture models (GMMs) were used to estimate the intensity distributions of foreground/background seeds [30]. Subsequently, the graph cuts algorithm [31, 32] was applied to the VOI supervoxels. (5) Finally, morphological opening, cavity filling, and median filtering were used to refine the segmented liver. After being resampled back to the original spatial resolution and size, the segmentation result was output for evaluation.

Bottom Line: Despite many years of research, automatic liver segmentation remains a challenging task.Thirty abdominal CT images were used to evaluate the accuracy and efficiency of the proposed algorithm.Experimental results show that the proposed method can detect the liver accurately with significant reduction of processing time, especially when dealing with diseased liver cases.

View Article: PubMed Central - PubMed

Affiliation: College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124, China.

ABSTRACT
Accurate segmentation of liver from abdominal CT scans is critical for computer-assisted diagnosis and therapy. Despite many years of research, automatic liver segmentation remains a challenging task. In this paper, a novel method was proposed for automatic delineation of liver on CT volume images using supervoxel-based graph cuts. To extract the liver volume of interest (VOI), the region of abdomen was firstly determined based on maximum intensity projection (MIP) and thresholding methods. Then, the patient-specific liver VOI was extracted from the region of abdomen by using a histogram-based adaptive thresholding method and morphological operations. The supervoxels of the liver VOI were generated using the simple linear iterative clustering (SLIC) method. The foreground/background seeds for graph cuts were generated on the largest liver slice, and the graph cuts algorithm was applied to the VOI supervoxels. Thirty abdominal CT images were used to evaluate the accuracy and efficiency of the proposed algorithm. Experimental results show that the proposed method can detect the liver accurately with significant reduction of processing time, especially when dealing with diseased liver cases.

No MeSH data available.


Related in: MedlinePlus